% Reviewer 1
\reviewer
% TODO =================
% 总结
% TODO √ R1总结
\begin{generalcomment}
The study proposes a refinement of permutation entropy (PE), referred to as amplitude-sensitive PE (ASPE), incorporating amplitude variations. The method is tested over very simple chaotic dynamics with the addition of spikes or Gaussian white noise, and applied to electroencephalographic traces and cardiac data.
% 该研究提出了一种改进的排列熵（PE），称为幅度敏感PE（ASPE），并纳入了幅度变化。该方法在非常简单的混沌动力学中进行了测试，并添加了尖峰或高斯白噪声，并应用于脑电图迹和心脏数据。
\end{generalcomment}

\begin{revmeta}[We sincerely thank the reviewer for the comprehensive and constructive feedback on our manuscript.]

Your detailed comments and suggestions have been invaluable in refining the scope, methodology, and presentation of our study. 

In response, we have carefully addressed each of the points raised. Beyond the specific modifications, we have conducted additional analyses and included new simulations to ensure the robustness and applicability of our proposed ASPE method. These efforts have allowed us to present a more thorough comparison with existing methods, incorporate a broader range of simulation scenarios, and clarify the rationale behind key methodological choices. We have also worked to improve the clarity and focus of the introduction and discussion, ensuring that our contributions to the field of complexity analysis are well-articulated and scientifically rigorous.

\end{revmeta}


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% 单问题
% TODO √ R1 模拟数据简单
\begin{*revcomment}
Simulated data are very simple and largely unrealistic given the type of physiological data processed in the study. Indeed, real data are very far from being generated by a purely deterministic model. More realistic simulations must be added involving stochastic systems.
% 模拟数据非常简单，且在研究中处理的生理数据类型中基本不现实。实际上，真实数据远非由纯确定性模型生成。必须添加涉及随机系统的更现实的模拟。
\end{*revcomment}

\begin{revresponse}[Thank you for your valuable feedback regarding the use of simulated data.]
%我们理解您的担忧，即最初使用的Logistic模型可能无法充分代表生理数据的复杂性和随机性。为了解决这个问题，我们在模拟中用更现实的自回归（AR(2)）过程取代了确定性Logistic模型。AR(2)模型包含随机成分，使其更适合模拟真实生理数据中观察到的可变性和复杂性。
We understand your concern that the originally used Logistic model may not sufficiently represent the complexity and stochastic nature of physiological data. To address this issue, we have replaced the deterministic Logistic model with a more realistic autoregressive (AR(2)) process in our simulations. The AR(2) model incorporates stochastic components, making it better suited to mimic the variability and complexity observed in real physiological data.

% 感谢您的反馈，这有助于我们提高研究的稳健性和清晰度。
We appreciate your feedback, which has helped us improve the robustness and clarity of our study.
\end{revresponse}


